Supplementary data for "Deep learning for industrial processes: Forecasting amine emissions from a carbon capture plant"
收藏资源简介：
A preliminary analysis of the data already has been discussed in 10.2139/ssrn.3812299. <strong>Raw data</strong> Raw measurement data is in the Excel files `day*_raw.xlsx`. <strong>Model</strong> Covariate and label scaler objects are serialized in joblib format in the following files: 20210812_y_transformer_co2_ammonia_reduced_feature_set 20210812_y_transformer__reduced_feature_set 20210812_x_scaler_reduced_feature_set Checkpoints of the models are in the `*.pth.tar` files. An example for loading the models is: <pre><code class="language-python">from pyprocessta.model.tcn import TCNModelDropout model_cov = TCNModelDropout( input_chunk_length=8, output_chunk_length=1, num_layers=5, num_filters=16, kernel_size=6, dropout=0.3, weight_norm=True, batch_size=32, n_epochs=100, log_tensorboard=True, optimizer_kwargs={"lr": 2e-4}, ) model_cov.load_from_checkpoint('20210814_2amp_pip_model_reduced_feature_set_darts')</code></pre> which assumes that the checkpoints are placed as `model_best.pth.tar` in a folder called `20210812_2amp_pip_model_reduced_feature_set_darts`.
